




'''simple docstring'''




def      __snake_case       (   lowercase   :							str			, lowercase   :							bool = False  ):




					if not isinstance(lowercase_			, lowercase_  ):
										snake_case_	=      f'''Expected string as input, found {type(lowercase_  )}'''
										raise ValueError(lowercase_  )
					if not isinstance(lowercase_			, lowercase_  ):
										snake_case_	=      f'''Expected boolean as use_pascal parameter, found {type(lowercase_  )}'''
										raise ValueError(lowercase_  )

					snake_case_	=      input_str.split("_"  )

					snake_case_	=      0 if use_pascal else 1

					snake_case_	=      words[start_index:]

					snake_case_	=      [word[0].upper() + word[1:] for word in words_to_capitalize]

					snake_case_	=      "" if use_pascal else words[0]

					return "".join([initial_word, *capitalized_words]  )


if __name__ == "__main__":
					from doctest import testmod

					testmod()













'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase        =							{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							["GLPNFeatureExtractor"]
  __lowerCAmelCase        =							["GLPNImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							[
      "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GLPNForDepthEstimation",
      "GLPNLayer",
      "GLPNModel",
      "GLPNPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase        =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




